{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "version": "3.7.3-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython2",
  "version": 2,
  "kernelspec": {
   "name": "python271664bitenvvirtualenv8836d05012704695b9eba33d763507b5",
   "display_name": "Python 2.7.16 64-bit ('env': virtualenv)"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Re-execution rate per publication year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import rc\n",
    "import pandas as pd\n",
    "\n",
    "# plot style\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_style({'font.family': 'Times New Roman'})\n",
    "\n",
    "sns.set_context(\"paper\", font_scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = pd.read_csv(\"data/total.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = total[total['publicationDate'] > 2014]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def success(el):\n",
    "    if pd.isna(el):\n",
    "        return el\n",
    "    if el == 'success':\n",
    "        return 1\n",
    "    if len(str(el))>1:\n",
    "        return 0\n",
    "    else:\n",
    "        el\n",
    "\n",
    "total['r32_success']=total[\"r32\"].apply(success)\n",
    "total['r36_success']=total[\"r36\"].apply(success)\n",
    "total['r40_success']=total[\"r40\"].apply(success)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers import autolabel, autolabel_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "barWidth = 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_func(temp, ptitle, plt):\n",
    "    names = list(temp.index)\n",
    "    temp.fillna(0.0, inplace=True)\n",
    "    temp['total'] = [i+j for i,j in zip(temp[1],temp[0])]\n",
    "\n",
    "    temp['s'] = [i* 100./ j  for i,j in zip(temp[1], temp['total'])]\n",
    "    temp['f'] = [i* 100./ j  for i,j in zip(temp[0], temp['total'])]\n",
    "\n",
    "    r= range(0,len(temp['s']))\n",
    "\n",
    "    lrects = sns.barplot(\n",
    "        x=r, y=\"s\", \n",
    "        data=temp,  \n",
    "        color=sns.color_palette(\"Set2\")[0], \n",
    "        label=\"Success\", \n",
    "        ax=plt)\n",
    "    hrects = sns.barplot(\n",
    "        x=r, y=\"f\", \n",
    "        data=temp, \n",
    "        bottom=temp['s'], \n",
    "        color=sns.color_palette(\"Set2\")[1], \n",
    "        label=\"Error\", ax=plt)\n",
    "    \n",
    "    plt.set_xticks(r)\n",
    "    plt.set_xticklabels(names)\n",
    "    #plt.set_xtickslabels(names) #, rotation=90, fontsize=15)\n",
    "    plt.legend(loc='best', ncol=1) #,prop={'size': 12})\n",
    "\n",
    "    autolabel(plt.patches[:6], plt)\n",
    "    autolabel_count(plt.patches[6:], plt, temp)\n",
    "\n",
    "    plt.set_title(ptitle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xlink:href=\"#TimesNewRomanPSMT-32\"/>\n       <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-35\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_8\">\n     <g id=\"line2d_20\">\n      <path clip-path=\"url(#pd4e09d8862)\" d=\"M 201.3155 67.60119 \nL 324.3685 67.60119 \n\" style=\"fill:none;stroke:#cccccc;stroke-linecap:round;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"text_36\">\n      <!-- 50 -->\n      <g style=\"fill:#262626;\" transform=\"translate(184.2155 70.65644)scale(0.088 -0.088)\">\n       <use xlink:href=\"#TimesNewRomanPSMT-35\"/>\n       <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_9\">\n     <g id=\"line2d_21\">\n      <path clip-path=\"url(#pd4e09d8862)\" d=\"M 201.3155 45.899286 \nL 324.3685 45.899286 \n\" style=\"fill:none;stroke:#cccccc;stroke-linecap:round;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"text_37\">\n      <!-- 75 -->\n      <g style=\"fill:#262626;\" transform=\"translate(184.2155 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xlink:href=\"#TimesNewRomanPSMT-39\"/>\n     <use x=\"175\" xlink:href=\"#TimesNewRomanPSMT-25\"/>\n    </g>\n   </g>\n   <g id=\"text_42\">\n    <!-- 27.1% -->\n    <g transform=\"translate(264.701768 83.089745)scale(0.065 -0.065)\">\n     <use xlink:href=\"#TimesNewRomanPSMT-32\"/>\n     <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-37\"/>\n     <use x=\"100\" xlink:href=\"#TimesNewRomanPSMT-2e\"/>\n     <use x=\"125\" xlink:href=\"#TimesNewRomanPSMT-31\"/>\n     <use x=\"175\" xlink:href=\"#TimesNewRomanPSMT-25\"/>\n    </g>\n   </g>\n   <g id=\"text_43\">\n    <!-- 38.7% -->\n    <g transform=\"translate(285.210602 73.020061)scale(0.065 -0.065)\">\n     <use xlink:href=\"#TimesNewRomanPSMT-33\"/>\n     <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-38\"/>\n     <use x=\"100\" xlink:href=\"#TimesNewRomanPSMT-2e\"/>\n     <use x=\"125\" xlink:href=\"#TimesNewRomanPSMT-37\"/>\n     <use x=\"175\" xlink:href=\"#TimesNewRomanPSMT-25\"/>\n    </g>\n   </g>\n   <g id=\"text_44\">\n    <!-- 33.5% -->\n    <g transform=\"translate(305.719435 77.534057)scale(0.065 -0.065)\">\n     <use xlink:href=\"#TimesNewRomanPSMT-33\"/>\n     <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-33\"/>\n     <use x=\"100\" xlink:href=\"#TimesNewRomanPSMT-2e\"/>\n     <use x=\"125\" xlink:href=\"#TimesNewRomanPSMT-35\"/>\n     <use x=\"175\" xlink:href=\"#TimesNewRomanPSMT-25\"/>\n    </g>\n   </g>\n   <g id=\"text_45\">\n    <!-- (157) -->\n    <g transform=\"translate(204.53062 107.878457)scale(0.065 -0.065)\">\n     <use xlink:href=\"#TimesNewRomanPSMT-28\"/>\n     <use x=\"33.300781\" xlink:href=\"#TimesNewRomanPSMT-31\"/>\n     <use x=\"83.300781\" xlink:href=\"#TimesNewRomanPSMT-35\"/>\n     <use x=\"133.300781\" xlink:href=\"#TimesNewRomanPSMT-37\"/>\n     <use x=\"183.300781\" xlink:href=\"#TimesNewRomanPSMT-29\"/>\n    </g>\n   </g>\n   <g id=\"text_46\">\n    <!-- (555) -->\n    <g transform=\"translate(225.039453 107.878457)scale(0.065 -0.065)\">\n     <use 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xlink:href=\"#TimesNewRomanPSMT-25\"/>\n    </g>\n   </g>\n   <g id=\"text_72\">\n    <!-- 27.0% -->\n    <g transform=\"translate(145.823435 214.992552)scale(0.065 -0.065)\">\n     <use xlink:href=\"#TimesNewRomanPSMT-32\"/>\n     <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-37\"/>\n     <use x=\"100\" xlink:href=\"#TimesNewRomanPSMT-2e\"/>\n     <use x=\"125\" xlink:href=\"#TimesNewRomanPSMT-30\"/>\n     <use x=\"175\" xlink:href=\"#TimesNewRomanPSMT-25\"/>\n    </g>\n   </g>\n   <g id=\"text_73\">\n    <!-- (131) -->\n    <g transform=\"translate(44.63462 239.694457)scale(0.065 -0.065)\">\n     <use xlink:href=\"#TimesNewRomanPSMT-28\"/>\n     <use x=\"33.300781\" xlink:href=\"#TimesNewRomanPSMT-31\"/>\n     <use x=\"83.300781\" xlink:href=\"#TimesNewRomanPSMT-33\"/>\n     <use x=\"133.300781\" xlink:href=\"#TimesNewRomanPSMT-31\"/>\n     <use x=\"183.300781\" xlink:href=\"#TimesNewRomanPSMT-29\"/>\n    </g>\n   </g>\n   <g id=\"text_74\">\n    <!-- (504) -->\n    <g 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xlink:href=\"#TimesNewRomanPSMT-32\"/>\n       <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-30\"/>\n       <use x=\"100\" xlink:href=\"#TimesNewRomanPSMT-31\"/>\n       <use x=\"150\" xlink:href=\"#TimesNewRomanPSMT-35\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_20\">\n     <g id=\"text_83\">\n      <!-- 2016 -->\n      <g style=\"fill:#262626;\" transform=\"translate(223.27875 257.2315)scale(0.088 -0.088)\">\n       <use xlink:href=\"#TimesNewRomanPSMT-32\"/>\n       <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-30\"/>\n       <use x=\"100\" xlink:href=\"#TimesNewRomanPSMT-31\"/>\n       <use x=\"150\" xlink:href=\"#TimesNewRomanPSMT-36\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_21\">\n     <g id=\"text_84\">\n      <!-- 2017 -->\n      <g style=\"fill:#262626;\" transform=\"translate(243.787583 257.2315)scale(0.088 -0.088)\">\n       <use xlink:href=\"#TimesNewRomanPSMT-32\"/>\n       <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-30\"/>\n       <use 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clip-path=\"url(#p672a28243a)\" d=\"M 201.3155 242.821 \nL 324.3685 242.821 \n\" style=\"fill:none;stroke:#cccccc;stroke-linecap:round;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"text_89\">\n      <!-- 0 -->\n      <g style=\"fill:#262626;\" transform=\"translate(188.6155 245.87625)scale(0.088 -0.088)\">\n       <use xlink:href=\"#TimesNewRomanPSMT-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_17\">\n     <g id=\"line2d_53\">\n      <path clip-path=\"url(#p672a28243a)\" d=\"M 201.3155 221.119095 \nL 324.3685 221.119095 \n\" style=\"fill:none;stroke:#cccccc;stroke-linecap:round;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"text_90\">\n      <!-- 25 -->\n      <g style=\"fill:#262626;\" transform=\"translate(184.2155 224.174345)scale(0.088 -0.088)\">\n       <use xlink:href=\"#TimesNewRomanPSMT-32\"/>\n       <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-35\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_18\">\n     <g id=\"line2d_54\">\n      <path clip-path=\"url(#p672a28243a)\" d=\"M 201.3155 199.41719 \nL 324.3685 199.41719 \n\" style=\"fill:none;stroke:#cccccc;stroke-linecap:round;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"text_91\">\n      <!-- 50 -->\n      <g style=\"fill:#262626;\" transform=\"translate(184.2155 202.47244)scale(0.088 -0.088)\">\n       <use xlink:href=\"#TimesNewRomanPSMT-35\"/>\n       <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_19\">\n     <g id=\"line2d_55\">\n      <path clip-path=\"url(#p672a28243a)\" d=\"M 201.3155 177.715286 \nL 324.3685 177.715286 \n\" style=\"fill:none;stroke:#cccccc;stroke-linecap:round;stroke-width:0.8;\"/>\n     </g>\n     <g id=\"text_92\">\n      <!-- 75 -->\n      <g style=\"fill:#262626;\" transform=\"translate(184.2155 180.770536)scale(0.088 -0.088)\">\n       <use xlink:href=\"#TimesNewRomanPSMT-37\"/>\n       <use x=\"50\" xlink:href=\"#TimesNewRomanPSMT-35\"/>\n      </g>\n     </g>\n    </g>\n    <g 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      "text/plain": "<Figure size 344.16x288 with 4 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(4.78,4))\n",
    "sns.set_context(\"paper\", font_scale=1)\n",
    "\n",
    "temp1 = total.groupby(\n",
    "    ['r32_success', 'publicationDate']).size().reset_index().pivot(\n",
    "    columns='r32_success', index='publicationDate', values=0)\n",
    "plot_func(temp1, ptitle=\"Success rate per year for R 3.2\", plt = ax1)\n",
    "\n",
    "temp2 = total.groupby(\n",
    "    ['r36_success', 'publicationDate']).size().reset_index().pivot(\n",
    "    columns='r36_success', index='publicationDate', values=0)\n",
    "plot_func(temp2, ptitle=\"Success rate per year for R 3.6\", plt=ax2)\n",
    "\n",
    "temp3 = total.groupby(\n",
    "    ['r40_success', 'publicationDate']).size().reset_index().pivot(\n",
    "    columns='r40_success', index='publicationDate', values=0)\n",
    "plot_func(temp3, ptitle=\"Success rate per year for R 4.0\", plt = ax3)\n",
    "\n",
    "temp4 = total.groupby(\n",
    "    ['success', 'publicationDate']).size().reset_index().pivot(\n",
    "    columns='success', index='publicationDate', values=0)\n",
    "plot_func(temp4, ptitle=\"Combined success rate per year\", plt = ax4)\n",
    "\n",
    "ax1.set(xlabel='', ylabel='Success rate (%)')\n",
    "ax2.set(xlabel='', ylabel='')\n",
    "ax3.set(xlabel='Year', ylabel='Success rate (%)')\n",
    "ax4.set(xlabel='Year', ylabel='')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"year-version.pdf\", bbox_inches = \"tight\", dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}